Real World Machinelearning – The Real-World ML Tutorial 2025-9

Descriptions

The Real-World Machine Learning Tutorial, Build a real-world ML system from A to Z. Learn how to design, develop, and deploy end-to-end ML products that help top startups and enterprises solve their business problems. This comprehensive tutorial covers the complete journey from understanding business problems to deploying ML solutions using MLOps best practices. You’ll master the 4 essential steps that transform any business problem into an ML solution: translating business problems into Machine Learning problems, preparing data and building robust pipelines, model prototyping with experimentation and training, and model operationalization with deployment and monitoring.

What you’ll learn

• Learn to translate business problems into Machine Learning solutions
• Build clean and robust data pipelines with Python to fetch, validate, transform and generate training data
• Create quick and powerful model baselines with feature engineering and boosting trees
• Optimize model hyper-parameters to maximize performance
• Transform ML model prototypes into fully working real-world batch-scoring systems
• Deploy ML solutions using Feature Store and GitHub actions
• Build frontend dashboards to show live predictions
• Implement monitoring systems for ML models
• Master the 4 steps from business problem to ML solution
• Apply MLOps best practices for deployment and monitoring

Who this course is for

• You’ve trained an ML model before and know how to prepare data and train a model in a notebook
• You want to learn how to design, implement and deploy a Machine Learning solution from A to Z
• You want to master the 4 steps that transform any business problem into an ML solution
• You want to land an ML Engineer job and stand out from the competition
• You don’t know how to turn ML prototypes into complete ML services
• You want to go one step further in your ML career

Specificatoin of The Real-World Machine Learning Tutorial

• Publisher : Real World Machinelearning
• Teacher : Pau Labarta Bajo
• Language : English
• Level : All Levels
• Number of Course : 45
• Duration : 4 hours and 3 minutes

Content of The Real-World Machine Learning Tutorial

1 Welcome
2 Development tools VSCode, Python Poetry and GitGitHub
3 Install VSCode and Python Poetry in your local machine
4 Create the Project Structure
5 Create local git repository
6 Create remote GitHub repository and connect it to the local one
7 Let’s understand the business problem
8 Plot the training data
9 Our raw data source the NYC taxi website
10 Step 1. Fetch raw data and validate it
11 Step 2. Transform raw validated data into time-series data
12 Plot the time-series data
13 Step 3. Transform time-series data into training data
14 Steps 1, 2 and 3. From raw data to training data + Code re-factoring!
15 Plot the training data (2)
16 How do you build a Supervised Machine Learning model
17 Split the dataset into training and test datasets
18 Baseline model 1
19 Baseline model 2
20 Baseline model 3
21 XGBoost model
22 LightGBM model
23 LightGBM + Feature engineering
24 LightGBM + Feature engineering + Hyper-parameter tuning
25 Batch-scoring ML service with a Feature Store
26 What is a Feature Store
27 Create a Serverless Feature Store with Hopsworks
28 Backfill Feature Store with Historical Data
29 Build the Feature Pipeline
30 Automate the execution of the Feature Pipeline using a GitHub action
31 Build the Model Training Pipeline
32 ML Frontend app using Streamlit
33 Inference functions
34 Build the Streamlit app – Part 1 inference code
35 Build the Streamlit app – Part 2 build the UI
36 Deploy the Streamlit app to Streamlit Cloud
37 Our plan
38 Create an inference pipeline to generate and store predictions in the store
39 The new (and way simpler) frontend Streamlit app – frontend.py
40 Create a monitoring dashboard with Streamlit
41 Deploy the monitoring dashboard to Streamlit Cloud
42 Why model re-training
43 Implementation
44 Karthikeya
45 Karthikeya

Pictures

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Installation Guide

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611 MB